Fastbook Save

The fastai book, published as Jupyter Notebooks

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The fastai book

These notebooks cover an introduction to deep learning, fastai, and PyTorch. fastai is a layered API for deep learning; for more information, see the fastai paper. Everything in this repo is copyright Jeremy Howard and Sylvain Gugger, 2020 onwards. A selection of chapters is available to read online here.

The notebooks in this repo are used for a MOOC and form the basis of this book, which is currently available for purchase. It does not have the same GPL restrictions that are on this repository.

The code in the notebooks and python .py files is covered by the GPL v3 license; see the LICENSE file for details. The remainder (including all markdown cells in the notebooks and other prose) is not licensed for any redistribution or change of format or medium, other than making copies of the notebooks or forking this repo for your own private use. No commercial or broadcast use is allowed. We are making these materials freely available to help you learn deep learning, so please respect our copyright and these restrictions.

If you see someone hosting a copy of these materials somewhere else, please let them know that their actions are not allowed and may lead to legal action. Moreover, they would be hurting the community because we're not likely to release additional materials in this way if people ignore our copyright.


Instead of cloning this repo and opening it on your machine, you can read and work with the notebooks using Google Colab. This is the recommended approach for folks who are just getting started -- there's no need to set up a Python development environment on your own machine, since you can just work directly in your web-browser.

You can open any chapter of the book in Colab by clicking on one of these links: Introduction to Jupyter | Chapter 1, Intro | Chapter 2, Production | Chapter 3, Ethics | Chapter 4, MNIST Basics | Chapter 5, Pet Breeds | Chapter 6, Multi-Category | Chapter 7, Sizing and TTA | Chapter 8, Collab | Chapter 9, Tabular | Chapter 10, NLP | Chapter 11, Mid-Level API | Chapter 12, NLP Deep-Dive | Chapter 13, Convolutions | Chapter 14, Resnet | Chapter 15, Arch Details | Chapter 16, Optimizers and Callbacks | Chapter 17, Foundations | Chapter 18, GradCAM | Chapter 19, Learner | Chapter 20, conclusion


If you make any pull requests to this repo, then you are assigning copyright of that work to Jeremy Howard and Sylvain Gugger. (Additionally, if you are making small edits to spelling or text, please specify the name of the file and a very brief description of what you're fixing. It's difficult for reviewers to know which corrections have already been made. Thank you.)


If you wish to cite the book, you may use the following:

title={Deep Learning for Coders with Fastai and Pytorch: AI Applications Without a PhD},
author={Howard, J. and Gugger, S.},
publisher={O'Reilly Media, Incorporated}
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